Classification of eucalyptus leaves: Combining color histogram feature extraction and decission tree algorithm
DOI:
https://doi.org/10.35335/cit.Vol16.2024.731.pp58-69Keywords:
Classification, Color Histogram, Decision Tree, EucalyptusAbstract
This research proposes an automatic approach to identify eucalyptus species based on leaf images using color histogram feature extraction and the Decision Tree algorithm. Eucalyptus is known as one of the most productive plants in the world with various uses in the timber, biofuel and pharmaceutical industries. However, its wide environmental adaptability and rapid growth pose challenges in identification and management. The proposed approach focuses on the use of Artificial Intelligence (AI) technology and image analysis to solve the identification problem. The color histogram feature extraction method is used to extract visual information about the color distribution of eucalyptus leaves. The Decision Tree algorithm is used to build a classification model based on the extracted features. Model evaluation is carried out using accuracy, precision, recall and F1-score metrics. The results showed that this approach was effective in identifying eucalyptus species, with a high level of accuracy. In addition, the development of this method offers opportunities for further applications in various fields, including forest mapping, mobile applications, and the timber industry. By combining advances in AI and image analysis, this research has the potential to become an important cornerstone of nature conservation and environmental sustainability efforts, and help strengthen natural resource management globally
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References
A. A. Garcia Rubio, A. Garg, F. Yang, L. Li, B. Ozdoganlar, and P. R. LeDuc, “Image segmentation and control of freeform 3D ice printing with computer vision,” Biophys. J., vol. 123, no. 3, Supplement 1, p. 548a, 2024, doi: https://doi.org/10.1016/j.bpj.2023.11.3318.
A. H. Rangkuti, Z. E. Rasjid, and D. J. Santoso, “Batik Image Classification Using Treeval and Treefit as Decision Tree Function in Optimizing Content Based Batik Image Retrieval,” Procedia Comput. Sci., vol. 59, pp. 577–583, 2015, doi: https://doi.org/10.1016/j.procs.2015.07.551.
A. Jajodia et al., “286P Improved diagnostic accuracy on MR imaging in post-surgical recurrent head and neck SCC lesions using decision tree classification system,” Ann. Oncol., vol. 31, p. S1353, 2020, doi: https://doi.org/10.1016/j.annonc.2020.10.280.
A. K. Sharadhi, V. Gururaj, S. P. Shankar, M. S. Supriya, and N. S. Chogule, “Face mask recogniser using image processing and computer vision approach,” Glob. Transitions Proc., vol. 3, no. 1, pp. 67–73, 2022, doi: https://doi.org/10.1016/j.gltp.2022.04.016.
A. Lailiyah, V. R. S. Nastiti, E. D. Wahyuni, and C. S. K. Aditya, “Klasifikasi Tingkat Kemampuan Adaptasi Siswa dalam Pembelajaran Online Menggunakan Decision Tree,” Techno. Com, vol. 23, no. 1, pp. 11–19, 2024.
A. Muzammil, U. Islam, Z. Hasan, and G. Probolinggo, “Fakultas Tarbiyah Universitas Islam Zainul Hasan Genggong Probolinggo 88,” Univ. Islam Zainul Hasan Genggong Probolinggo, vol. 4, no. 2, pp. 88–112, 2022, [Online]. Available: file:///C:/Users/62831/Downloads/ahmad-muzammil-pba-1.pdf.
A. Pramesta, “Perancangan Aplikasi Identifikasi Penyakit Kerdil dan Leaf Blight Pada Tanaman Lada Berdasarkan Citra Daun Berbasis Website,” Fakultas Sains dan Teknologi UIN Syarif HIdayatullah Jakarta.
A. Uryasheva, A. Kalashnikova, D. Shadrin, K. Evteeva, E. Moskovtsev, and N. Rodichenko, “Computer vision-based platform for apple leaves segmentation in field conditions to support digital phenotyping,” Comput. Electron. Agric., vol. 201, p. 107269, 2022, doi: https://doi.org/10.1016/j.compag.2022.107269.
A. Y. Rahman and others, “Klasifikasi Citra Burung Lovebird Menggunakan Decision Tree dengan Empat Jenis Evaluasi,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 4, pp. 688–696, 2021.
Amri, S., & Sihotang, J. (2023). Impact of Poverty Reduction Programs on Healthcare Access in Remote Ar-eas: Fostering Community Development for Sustainable Health. Law and Economics, 17(3), 170-185.
B. Dunia, Pengembangan Pertanian Secara Berkelanjutan di Lahan. Bank Dunia, 2021.
B. Sreedha, P. R. Nair, and R. Maity, “Non-invasive early diagnosis of jaundice with computer vision,” Procedia Comput. Sci., vol. 218, pp. 1321–1334, 2023, doi: https://doi.org/10.1016/j.procs.2023.01.111.
C. R. Pereira et al., “A new computer vision-based approach to aid the diagnosis of Parkinson’s disease,” Comput. Methods Programs Biomed., vol. 136, pp. 79–88, 2016, doi: https://doi.org/10.1016/j.cmpb.2016.08.005.
C.-C. Yang et al., “Application of decision tree technology for image classification using remote sensing data,” Agric. Syst., vol. 76, no. 3, pp. 1101–1117, 2003, doi: https://doi.org/10.1016/S0308-521X(02)00051-3.
D. ?umi?, D. Ke?o, and Z. Mašeti?, “Automatization of Microscopy Malaria Diagnosis Using Computer Vision and Random Forest Method,” IFAC-PapersOnLine, vol. 55, no. 4, pp. 80–84, 2022, doi: https://doi.org/10.1016/j.ifacol.2022.06.013.
D. Fatimah and E. Rahmawati, “Penggunaan Metode Decision Tree dalam Rancang Bangun Sistem Prediksi untuk Kelulusan Mahasiswa,” J. Algoritm., vol. 18, pp. 553–561, 2022, doi: 10.33364/algoritma/v.18-2.932.
D. Leni, “Pemilihan Algoritma Machine Learning Yang Optimal Untuk Prediksi Sifat Mekanik Aluminium,” J. Engine Energi, Manufaktur, dan Mater., vol. 7, no. 1, pp. 35–44, 2023.
D. P. dan P. K. Demak, “MENGENAL MANFAAT TANAMAN EUCALYPTUS,” Dinas Pertanian dan Pangan Kabupaten Demak, 2022. https://dinpertanpangan.demakkab.go.id/?p=4625 (accessed Feb. 14, 2024).
D. Septhya et al., “Implementasi Algoritma Decision Tree dan Support Vector Machine untuk Klasifikasi Penyakit Kanker Paru: Implementation of Decision Tree Algorithm and Support Vector Machine for Lung Cancer Classification,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 1, pp. 15–19, 2023.
E. F. Elli, P. C. Sentelhas, and F. D. Bender, “Impacts and uncertainties of climate change projections on Eucalyptus plantations productivity across Brazil,” For. Ecol. Manage., vol. 474, p. 118365, 2020, doi: https://doi.org/10.1016/j.foreco.2020.118365.
F. A. Astuti, “Pemanfaatan Teknologi Artificial Intelligence untuk Penguatan Kesehatan dan Pemulihan Ekonomi Nasional,” J. Sist. Cerdas, vol. 4, no. 1, pp. 25–34, 2021, doi: 10.37396/jsc.v4i1.124.
H. Li, N. Zeng, P. Wu, and K. Clawson, “Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision,” Expert Syst. Appl., vol. 207, p. 118029, 2022, doi: https://doi.org/10.1016/j.eswa.2022.118029.
I. D. Id and U. Riau, “PERANCANGAN DAN PENERAPAN ALGORITMA STEMMING KATA Sebagai salah satu syarat untuk memperoleh gelar Sarjana dalam bidang Ilmu Komputer,” no. March, 2018, doi: 10.13140/RG.2.2.16709.55526.
I. Gunawan, M. A. Liman, G. Ryan, and F. Purnomo, “Face Mask Detection for COVID-19 Prevention using Computer Vision,” Procedia Comput. Sci., vol. 227, pp. 1143–1152, 2023, doi: https://doi.org/10.1016/j.procs.2023.10.626.
J. Im and J. R. Jensen, “A change detection model based on neighborhood correlation image analysis and decision tree classification,” Remote Sens. Environ., vol. 99, no. 3, pp. 326–340, 2005, doi: https://doi.org/10.1016/j.rse.2005.09.008.
J. Jaworek-Korjakowska, M. H. Yap, D. Bhattacharjee, P. Kleczek, A. Brodzicki, and M. Gorgon, “Chapter 3 - Deep neural networks and advanced computer vision algorithms in the early diagnosis of skin diseases,” in State of the Art in Neural Networks and Their Applications, A. S. El-Baz and J. S. Suri, Eds. Academic Press, 2023, pp. 47–81.
J. O. Ditinjau et al., “The Ushuluddin International Student Conference,” vol. 1, no. 2, 2023, [Online]. Available: http://proceedings.radenfatah.ac.id/index.php/UInScof2022/manageIssues#future.
J. Supriatna, Konservasi Biodiversitas: Teori dan Praktik di Indonesia. Yayasan Pustaka Obor Indonesia, 2018.
J. Zhang, S. Qian, and C. Tan, “Automated bridge surface crack detection and segmentation using computer vision-based deep learning model,” Eng. Appl. Artif. Intell., vol. 115, p. 105225, 2022, doi: https://doi.org/10.1016/j.engappai.2022.105225.
K. Topouzelis and A. Psyllos, “Oil spill feature selection and classification using decision tree forest on SAR image data,” ISPRS J. Photogramm. Remote Sens., vol. 68, pp. 135–143, 2012, doi: https://doi.org/10.1016/j.isprsjprs.2012.01.005.
L. Alonso, A. Rodríguez-Dorna, J. Picos, F. Costas, and J. Armesto, “Automatic differentiation of Eucalyptus species through Sentinel-2 images, Worldview-3 images and LiDAR data,” ISPRS J. Photogramm. Remote Sens., vol. 207, pp. 264–281, 2024, doi: https://doi.org/10.1016/j.isprsjprs.2023.12.010.
L. N. Rachmadi, A. P. Wibawa, and U. Pujianto, “Rekomendasi Jurusan dengan Menggunakan Decision Tree pada Sistem Penerimaan Peserta Didik Baru SMK Widya Dharma Turen,” Belantika Pendidik., vol. 4, no. 1, pp. 29–36, 2021.
M. A. Wirya, “Deteksi Penyakit Alzheimer Pada Citra Magnetic Resonance Imaging Menggunakan Machine Learning dengan Metode Convolutional Neural Network,” Fakultas Sains dan Teknologi UIN Syarif Hidayatullah Jakarta.
M. Siahaan, C. H. Jasa, K. Anderson, M. V. Rosiana, S. Lim, and W. Yudianto, “Penerapan Artificial Intelligence (AI) Terhadap Seorang Penyandang Disabilitas Tunanetra,” J. Inf. Syst. Technol., vol. 1, no. 2, pp. 186–193, 2020, [Online]. Available: https://journal.uib.ac.id/index.php/joint/article/view/4322.
M. Y. Salam, “Ekstraksi Minyak Atsiri dari Daun Eucalyptus grandis Menggunakan isolat Selulase,” Skripsi, vol. 2, pp. 1–89, 2018.
M. Zortea et al., “Performance of a dermoscopy-based computer vision system for the diagnosis of pigmented skin lesions compared with visual evaluation by experienced dermatologists,” Artif. Intell. Med., vol. 60, no. 1, pp. 13–26, 2014, doi: https://doi.org/10.1016/j.artmed.2013.11.006.
N. F. Amandari, I. Maulidina, and R. T. M. Akbar, “Studi Keanekaragaman dan Analisis Risiko Tumbuhan Invasif di Kawasan Taman Kelurahan Mekarmulya Kecamatan Cinambo, Kota Bandung, Provinsi Jawa Barat,” J. Ris. Rumpun Ilmu Tanam., vol. 2, no. 2, pp. 51–61, 2023.
N. Hidayati, “EKSTRAKSI FITUR DENGAN COLOR HISTOGRAM DAN CLASSIFIER RANDOM FOREST PADA CITRA KUPU-KUPU,” JAMI J. Ahli Muda Indones., vol. 4, no. 2, pp. 42–51, 2023.
N. Sutrisno and N. Heryani, “Teknologi konservasi tanah dan air untuk mencegah degradasi lahan pertanian berlereng,” J. Penelit. dan Pengemb. Pertan., vol. 32, no. 3, 2014.
O. C. Campoe et al., “Climate and genotype influences on carbon fluxes and partitioning in Eucalyptus plantations,” For. Ecol. Manage., vol. 475, no. May, 2020, doi: 10.1016/j.foreco.2020.118445.
P. W. Rahayu et al., Buku Ajar Data Mining. PT. Sonpedia Publishing Indonesia, 2024.
R. Dijaya and H. Setiawan, “Buku Ajar Pengolahan Citra Digital,” Umsida Press, pp. 1–85, 2023.
R. N. Ramadhon, A. Ogi, A. P. Agung, R. Putra, S. S. Febrihartina, and U. Firdaus, “Implementasi Algoritma Decision Tree untuk Klasifikasi Pelanggan Aktif atau Tidak Aktif pada Data Bank,” Karimah Tauhid, vol. 3, no. 2, pp. 1860–1874, 2024.
S. A. Pratiwi, A. Fauzi, S. A. P. Lestari, and Y. Cahyana, “Prediksi Persediaan Obat Pada Apotek Menggunakan Algoritma Decision Tree,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 4, no. 4, pp. 2381–2388, 2024.
S. Lee et al., “Multimodal integration of neuroimaging and genetic data for the diagnosis of mood disorders based on computer vision models,” J. Psychiatr. Res., vol. 172, pp. 144–155, 2024, doi: https://doi.org/10.1016/j.jpsychires.2024.02.036.
S. M. G. Rocha et al., “Influence of climatic variations on production, biomass and density of wood in eucalyptus clones of different species,” For. Ecol. Manage., vol. 473, p. 118290, 2020, doi: https://doi.org/10.1016/j.foreco.2020.118290.
Sihotang, J. (2019). Implementation of Gray Level Transformation Method for Sharping 2D Images. INFOKUM, 8(1, Desembe), 16-19.
Sihotang, J. (2020). Analysis of service satisfaction level using rough set algorithm. Infokum, 8(2, Juni), 55-56.
Sihotang, J. (2020). Analysis Of Shortest Path Determination By Utilizing Breadth First Search Algorithm. Jurnal Info Sains: Informatika dan Sains, 10(2), 1-5.
Sihotang, J. (2023). Analysis Of The Differences In The Use Of Zoom And Google Meet In Learning At Putra Abadi Langkat University. Instal: Jurnal Komputer, 15(02), 474-479.
Sihotang, J. (2023). Optimization of Inventory Ordering Decision in Retail Business using Exponential Smoothing Approach and Decision Support System. International Journal of Mechanical Computational and Manufacturing Research, 12(2), 46-52.
Sihotang, J., Alesha, A., Batubara, J., Gorat, S. E., & Panjaitan, F. S. (2022). A hybrid approach for adaptive fuzzy network partitioning and rule generation using rough set theory: Improving data-driven decision making through accurate and interpretable rules. International Journal of Enterprise Modelling, 16(1), 12-22.
Sihotang, J., Panjaitan, E. S., & Yunis, R. (2020). Evaluation of Information Technology Governance by Using CobIT 5 Framework at Higher Education. Jurnal Mantik, 4(3), 2194-2203.
T. A. Nasution, Yeni Herdiyeni, Wisnu Ananta Kusuma, Budi Tjahjono, and Iskandar Zulkarnaen Siregar, “Kecerdasan Buatan untuk Monitoring Hama dan Penyakit pada Tanaman Eucalyptus: Systematic Literature Review,” J. Ilmu Komput. dan Agri-Informatika, vol. 10, no. 2, pp. 224–237, 2023, doi: 10.29244/jika.10.2.224-237.
T. Novianti, S. A. Mandati, and E. K. Andana, “Peningkatan Evaluasi Risiko Kredit Menggunakan Decision Tree C 4.5,” MINE-TECH J. Manuf. Ind. Eng. & Technol., vol. 2, no. 2, pp. 1–9, 2023.
V. B. Panunggul et al., PENGANTAR ILMU PERTANIAN. Penerbit Widina, 2023.
V. Siahaan and R. H. Sianipar, Panduan praktis dan komplet Pemrosesan Citra Digital dengan Matlab. Balige Publishing, 2020.
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Copyright (c) 2024 Sarifah Agustiani, Rahmat Hidayat, Yoseph Tajul Arifin, Haryani, Siti Marlina
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